RESEARCH ON WIND POWER PREDICTION METHODS BASED ON WAA-ICEEMDAN AND TENT-ESN

Yang Ningning; Zhang Tian; Wu Chaojun; Zhang Chen; Dong Minjie; Ma Zhirui

doi:10.19912/j.0254-0096.tynxb.2025-0919

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (12) : 328-336. DOI: 10.19912/j.0254-0096.tynxb.2025-0919

RESEARCH ON WIND POWER PREDICTION METHODS BASED ON WAA-ICEEMDAN AND TENT-ESN

Yang Ningning¹, Zhang Tian¹, Wu Chaojun², Zhang Chen¹, Dong Minjie¹, Ma Zhirui¹

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Abstract

Aiming at the problems of non-stationarity, obvious seasonal fluctuation and poor real-time performance in short-term wind power forecasting, this paper proposes a wind power prediction model combining WAA-ICEEMDAN and echo state network improved by Tent chaotic map. The weighted average operator is adopted to optimize ICEEMDAN to restrain mode mixing, and the Tent map replaces the activation function of ESN to strengthen the capture of abrupt time-series features. Simulations are performed on full-year four-season measured data from a wind farm, with comparative experiments against tanh-ESN, VMD-ESN, Transformer, Informer and Autoformer. The results show that the proposed model achieves the optimal RMSE, MAE and MAPE under all seasonal conditions, and its prediction errors are barely affected by seasons. With an inference time of approximately 3.2 s, the model balances prediction accuracy, seasonal robustness and low computational latency, and is applicable to short-term real-time wind power forecasting of wind farms.

Key words

wind power / power prediction / echo state network / Tent chaotic mapping / weighted average operator / improved fully adaptive noise set empirical mode decomposition

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Yang Ningning, Zhang Tian, Wu Chaojun, Zhang Chen, Dong Minjie, Ma Zhirui. RESEARCH ON WIND POWER PREDICTION METHODS BASED ON WAA-ICEEMDAN AND TENT-ESN[J]. Acta Energiae Solaris Sinica. 2026, 47(12): 328-336 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0919

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